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Great. I’ll put together a deeply technical report on the latest AI-based techniques for automating research proposal writing and reviewing—focusing mainly on STEM fields, but also touching on others where relevant. I’ll include both commercial and academic tools, explore challenges and solutions, and highlight promising research directions aimed at fully autonomous proposal generation. I’ll also provide links to GitHub repositories and demos where available.

I’ll let you know as soon as the report is ready for your review.

AI-Powered Automation of Research Proposal Writing and Review in STEM

Introduction

Writing a competitive research proposal is a critical yet time-consuming task for scientists. In STEM fields (and increasingly in others), researchers are turning to AI-based tools to streamline everything from brainstorming ideas to polishing final drafts. Recent advances in large language models (LLMs) like GPT-4 have sparked a proliferation of systems that can generate text, search litera

@chitchcock
chitchcock / 20111011_SteveYeggeGooglePlatformRant.md
Created October 12, 2011 15:53
Stevey's Google Platforms Rant

Stevey's Google Platforms Rant

I was at Amazon for about six and a half years, and now I've been at Google for that long. One thing that struck me immediately about the two companies -- an impression that has been reinforced almost daily -- is that Amazon does everything wrong, and Google does everything right. Sure, it's a sweeping generalization, but a surprisingly accurate one. It's pretty crazy. There are probably a hundred or even two hundred different ways you can compare the two companies, and Google is superior in all but three of them, if I recall correctly. I actually did a spreadsheet at one point but Legal wouldn't let me show it to anyone, even though recruiting loved it.

I mean, just to give you a very brief taste: Amazon's recruiting process is fundamentally flawed by having teams hire for themselves, so their hiring bar is incredibly inconsistent across teams, despite various efforts they've made to level it out. And their operations are a mess; they don't real

@Pusnow
Pusnow / CS 분야 우수 학술대회 목록.csv
Last active July 21, 2026 01:39
CS 분야 우수 학술대회 목록
약자 한국정보과학회 (2024) BK21플러스 IF (2018) KAIST CS (2025) SNU CSE (2024.4) POSTECH CSE (2026.1) 평균 (정규화) 학회명 DBLP Key
AAAI 최우수 4 O O 최우수 1.00 AAAI Conference on Artificial Intelligence (AAAI) conf/aaai
AAMAS 우수 2 0.20 International Conference on Autonomous Agents and Multiagent Systems (AAMAS) conf/ifaamas
ACCV 우수 1 우수 0.25 Asian Conference on Computer Vision (ACCV) conf/accv
ACL 최우수 4 O O 최우수 1.00 Annual Meeting of the Association for Computational Linguistics (ACL) conf/acl
ACL Findings 우수 우수 0.20 Findings of ACL series/findacl
ACNS 우수 0.10 International Conference on Applied Cryptography and Network Security (ACNS) conf/acns
ACSAC 우수 2 우수 0.30 Annual Computer Security Applications Conference (ACSAC) conf/acsac
AIED 우수 0.10 International Conference on Artificial Intelligence in Education (AIED) conf/aied
AISTATS 우수 1 우수 0.25 International Conference on Artificial Intelligence and Statistics (AISTATS) conf/aistats
@k16shikano
k16shikano / SKILL.md
Last active July 21, 2026 01:35
cognitive-rhythm-writing/SKILL.md
name cognitive-rhythm-writing
description 説明的な文章に緩急を設計するための規範。緩急を装飾ではなく認知モードの切替(観察→逡巡→断定→再観察)と未回収の緊張の管理として扱い、文の拍、段落の密度波形、節の入り方、緩みと駄文の判別、執筆後の機械的な点検手順を定める。読み物として読ませたい章・記事・解説文を生成するとき、または「密度はあるが平坦でおもしろくない」文章を診断・修正するときに使用する。

認知リズムを生むための日本語ライティング規範

密度の高い文章が退屈になるのは、情報が多いからではなく、全文が同じ認知モードで書かれているからである。 この規範は、読者の認知モード(観察する、迷う、確信する、確かめ直す)を意図的に切り替え、常に「続きを読む理由」を維持することで、読み進める推進力を作る。

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
@TheRealMJP
TheRealMJP / Tex2DCatmullRom.hlsl
Last active July 21, 2026 01:13
An HLSL function for sampling a 2D texture with Catmull-Rom filtering, using 9 texture samples instead of 16
// The following code is licensed under the MIT license: https://gist.github.com/TheRealMJP/bc503b0b87b643d3505d41eab8b332ae
// Samples a texture with Catmull-Rom filtering, using 9 texture fetches instead of 16.
// See http://vec3.ca/bicubic-filtering-in-fewer-taps/ for more details
float4 SampleTextureCatmullRom(in Texture2D<float4> tex, in SamplerState linearSampler, in float2 uv, in float2 texSize)
{
// We're going to sample a a 4x4 grid of texels surrounding the target UV coordinate. We'll do this by rounding
// down the sample location to get the exact center of our "starting" texel. The starting texel will be at
// location [1, 1] in the grid, where [0, 0] is the top left corner.
float2 samplePos = uv * texSize;